title: Do non-glycaemic markers add value to plasma glucose and hemoglobin a1c in predicting diabetes? Yuport health checkup center study.
abstract: BACKGROUND
  Many markers have been indicated as predictors of type 2 diabetes. However, the question of whether or not non-glycaemic (blood) biomarkers and non-blood biomarkers have a predictive additive utility when combined with glycaemic (blood) biomarkers is unknown. The study aim is to assess this additive utility in a large Japanese population.
  
  
  METHODS
  We used data from a retrospective cohort study conducted from 1998 to 2002 for the baseline and 2002 to 2006 for follow-up, inclusive of 5,142 men (mean age of 51.9 years) and 4,847 women (54.1 years) at baseline. The cumulative incidence of diabetes [defined either as a fasting plasma glucose (FPG) ≥7.00 mmol/l or as clinically diagnosed diabetes] was measured. In addition to glycaemic biomarkers [FPG and hemoglobin A1c (HbA1c)], we examined the clinical usefulness of adding non-glycaemic biomarkers and non-blood biomarkers, using sensitivity and specificity, and the area under the curve (AUC) of the receiver operating characteristics.
  
  
  RESULTS
  The AUCs to predict diabetes were 0.874 and 0.924 for FPG, 0.793 and 0.822 for HbA1c, in men and women, respectively. Glycaemic biomarkers were the best and second-best for diabetes prediction among the markers. All non-glycaemic markers (except uric acid in men and creatinine in both sexes) predicted diabetes. Among these biomarkers, the highest AUC in the single-marker analysis was 0.656 for alanine aminotransferase (ALT) in men and 0.740 for body mass index in women. The AUC of the combined markers of FPG and HbA1c was 0.895 in men and 0.938 in women, which were marginally increased to 0.904 and 0.940 when adding ALT, respectively.
  
  
  CONCLUSIONS
  AUC increments were marginal when adding non-glycaemic biomarkers and non-blood biomarkers to the classic model based on FPG and HbA1c. For the prediction of diabetes, FPG and HbA1c are sufficient and the other markers may not be needed in clinical practice.
authors.affiliation: Department of Public Health and Health Policy, Institute of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan. saori_ksm@ybb.ne.jp
authors.forename: Saori
authors.initials: S
authors.lastname: Kashima
authors.forename: Kazuo
authors.initials: K
authors.lastname: Inoue
authors.forename: Masatoshi
authors.initials: M
authors.lastname: Matsumoto
authors.forename: Kimihiko
authors.initials: K
authors.lastname: Akimoto
mesh_terms: D000328:Adult; D000368:Aged; D000410:Alanine Transaminase / Q000097:blood; D001219:Aspartate Aminotransferases / Q000097:blood; D015415:Biomarkers / Q000097:blood*; D001786:Blood Glucose / Q000378:metabolism*; D001794:Blood Pressure; D015992:Body Mass Index; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000175:diagnosis / Q000453:epidemiology; D005260:Female; D005500:Follow-Up Studies; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D015994:Incidence; D007564:Japan / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D012189:Retrospective Studies
chemical_list: D000410:Alanine Transaminase; D001219:Aspartate Aminotransferases; D015415:Biomarkers; D001786:Blood Glucose; D006442:Glycated Hemoglobin